In this thesis, we study the neurocomputation by implementing two different neuron models. One is a semi magnetic micro p-n wire that emulates nerve fibres and supports the electrical propagation and regeneration. The other is a silicon neuron based on Hodgkin-Huxley conductance model that can generate spatiotemporal spiking patterns. The former model focuses on the spatial propagation of electrical pulses along a transmission line and presents the thesis that action potentials may be represented by solitary waves. The later model focuses on the dynamical properties such as how the output patterns of the active networks adapt to external stimulus. To demonstrate the dynamical properties of spiking networks, we present a central pattern generator (CPG) network with winnerless competition architecture. The CPG consists of three silicon neurons which are connected via reciprocally inhibitory synapses. The network of three neurons was stimulated with current steps possessing different time delays and that the voltage oscillations of the three neurons were recorded as a function of the strengths of inhibitory synaptic interconnections and internal parameters of neurons, such as voltage thresholds, time delays, etc. The architecture of the network is robust and sensitively depends on the stimulus. Stimulus dependent rhythms can be generated by the CPG network. The stimulus-dependent sequential switching between collective modes of oscillations in the network can explain the fundamental contradiction between sensitivity and robustness to external stimulus and the mechanism of pattern memorization. We successfully apply the CPG in modulating the heart rate of animal models (rats). The CPG was stimulated with respiratory signals and generated tri-phasic patterns corresponding to the respiratory cycles. The tri-phasic stimulus from the CPG was used to synchronize the heart rate with respiration. In this way, we artificially induce the respiratory sinus arrhythmia (RSA), which refers to the heart rate fluctuation in synchrony with respiration. RSA is lost in heart failure. Our CPG paves to way to novel medical devices that can provide a therapy for heart failure.

Made available in DSpace on 2014-06-12T23:01:00Z (GMT). No. of bitstreams: 2 arquivo8618_1.pdf: 1604073 bytes, checksum: d9765092ab2a50cd8b1426f73b5ed129 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2006 / Este estudo é uma revisão narrativa da literatura sobre os paradigmas computacionais, as modelagens naturais conexionistas e a investigação dos fenômenos psicopatológicos. O objetivo geral foi realizar uma coleta de informações sobre os trabalhos publicados, até então, que contemplassem os modelos de processamento de informações no cérebro humano, a analogia com Redes Neurais Artificiais e a aplicação de métodos investigativos nas psicopatologias. A seleção dos estudos foi baseada principalmente pesquisas em bancos de dados digitais: Medline, Períodos CAPES, MIT Search, Scholar Google e PsychInfo, usando os descritores neural networks, neurocomputation, psychopathology, connectionism, mood disorders, depression, cognition e artificial intelligence, em mecanismos de busca digital. Foram selecionados os estudos considerando os critérios de inclusão a partir dos descritores, o aspecto cronológico, a adequação e pertinência dos estudos e o impacto destes artigos na comunidade científica. A literatura clássica também foi incluída. O estudo buscou estabelecer relações entre as pesquisas que utilizaram ferramentas computacionais, visando a criação de modelos que simularam o funcionamento cognitivo do cérebro humano. Destes modelos, as Redes Neurais Artificiais Conexionistas (RNA) mostraram-se as mais promissoras dentre as demais. Conclui-se que as investigações dos fenômenos psicopatológicos baseadas em modelagem computacional conexionista constituem em uma importante estratégia para compreensão do funcionamento da mente humana e de como se processam as alterações psíquicas